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2025 | OriginalPaper | Buchkapitel

Federated Learning for Personalized Tourism Promotion: Balancing Recommendation Accuracy and User Privacy

verfasst von : S. Amutha, P. Salini

Erschienen in: Innovative Computing and Communications

Verlag: Springer Nature Singapore

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Abstract

Deep learning allows the recommender system to discover deep patterns and representations from data, capturing complex interactions between users and objects across multiple domains. Deep learning manages massive amounts of data and produces reliable representations to have effect on the quality of cross-domain recommender system. We utilize a federated learning algorithm to ensure that each domain retains control over its data and contributes to the global improvement of the suggestion, while also avoiding the sharing of sensitive information. To create major scenarios, privacy concerns are restricted to sharing the data. We recommend a novel model for personalizing the recommendation system by embedding the techniques of Federated Learning in tourism promotion. This model enables collaborative training across multiple domains to provide diverse recommendations and secure data.

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Natarajan, S., et al. (2022). CD-SemMF: Cross-domain semantic relatedness based matrix factorization model enabled with linked open data for user cold start issue. IEEE Access, 10, 52955–52970. https://doi.org/10.1109/ACCESS.2022.3175566CrossRef

Mistry, D., Mridha, M. F., Safran, M., Alfarhood, S., Saha, A. K., & Che, D. (2023). Privacy-preserving on-screen activity tracking and classification in E-learning using federated learning. IEEE Access, 11, 79315–79329. https://doi.org/10.1109/ACCESS.2023.3299331

Zhang, S., Ma, X., Wang, Y., Zhou, Y., & Yu, D. (2022). An embedding and interactions learning approach for ID feature in deep recommender system (Vol. 210).

Trivedi, S., Chheda, R., Bohara, D., & Bohara, D. (2023). ScienceDirect music recommendation based on affective image content analysis music recommendation based on affective content (pp. 1–10).

Yera, R., Alzahrani, A. A., & Martínez, L. (2022). A fuzzy content-based group recommender system with dynamic selection of the aggregation functions. International Journal of Approximate Reasoning, 150, 273–296. https://doi.org/10.1016/j.ijar.2022.08.015CrossRef

Stratigi, M., Pitoura, E., & Stefanidis, K. (2023). SQUIRREL : A framework for sequential group recommendations through reinforcement learning. Information Systems, 112, 102128. https://doi.org/10.1016/j.is.2022.102128CrossRef

Li, Z., et al. (2022). HML4Rec: Hierarchical meta-learning for cold-start recommendation in flash sale e-commerce. Knowledge-Based System, 255, 109674. https://doi.org/10.1016/j.knosys.2022.109674CrossRef

Elahi, M., Khosh Kholgh, D., Kiarostami, M. S., Oussalah, M., & Saghari, S. (2023). Hybrid recommendation by incorporating the sentiment of product reviews. Information Science (NY), 625, pp. 738–756. https://doi.org/10.1016/j.ins.2023.01.051

Imakura, A., Tsunoda, R., Kagawa, R., Yamagata, K., & Sakurai, T. (2023). DC-COX: Data collaboration cox proportional hazards model for privacy-preserving survival analysis on multiple parties (Vol. 137).

10.

Yan, D., Zhao, Y., Yang, Z., Jin, Y., & Zhang, Y. (2022). FedCDR: Privacy-preserving federated cross-domain recommendation (Vol. 8, pp. 552–560).

11.

Gupta, K. D., Sadman, N., Sadmanee, A., Sarker, M. K., & George, R. (2022). Behavioral recommendation engine driven by only non-identifiable user data. Machine Learning with Application, 11, 100442. https://doi.org/10.1016/j.mlwa.2022.100442

12.

Frimpong, S. A., et al. (2023). RecGuard: An efficient privacy preservation blockchain-based system for online social network users. Blockchain Research Application, 4(1). https://doi.org/10.1016/j.bcra.2022.100111

13.

Kotelnikova, A. V., Vychegzhanin, S. V., & Kotelnikov, E. V. (2023). Cross-domain sentiment analysis based on small in-domain fine-tuning. IEEE Access, 11, 41061–41074. https://doi.org/10.1109/ACCESS.2023.3269720CrossRef

14.

Goldsteen, A., Saadi, O., Shmelkin, R., Shachor, S., & Razinkov, N. (2023). AI privacy toolkit. SoftwareX, 22, 101352. https://doi.org/10.1016/j.softx.2023.101352CrossRef

15.

Guo, H., Guo, Z., & Liu, Z. (2023). Personalized recommendation model based on improved GRU network in big data environment. Journal of Electrical Computing Engineering, 2023. https://doi.org/10.1155/2023/3162220

16.

Murdock, I., Carley, K. M., & Yağan, O. (2023). Identifying cross-platform user relationships in 2020 U.S. election fraud and protest discussions (Vol. 33).

17.

Ma, H., Chen, X., & Lin, L. Triple sequence learning for cross-domain recommendation (Vol. 1, no. 1). Association for Computing Machinery.

18.

Song, K., Sun, Q., Xu, C., Zheng, K., & Yang, Y. (2023). Self-supervised multi-modal sequential recommendation (Vol. 1, no. 1). Association for Computing Machinery [online]. Available http://arxiv.org/abs/2304.13277

19.

Cao, J., & Wang, B. Towards universal cross-domain recommendation (pp. 78–86). https://doi.org/10.1145/3539597.3570366

20.

Ezaldeen, H., Bisoy, S. K., Misra, R., & Alatrash, R. (2023). Semantics aware intelligent framework for content-based e-learning recommendation (Vol. 3).

21.

Li, H., Qiao, M., & Peng, S. (2022). Research on the recommendation algorithm of rural tourism routes based on the fusion model of multiple data sources. Discrete Dynamics in Nature and Society, 2022. https://doi.org/10.1155/2022/2262148

22.

Yang, T., Gao, Y., Huang, Z., & Liu, Y. (2023). UPTDNet: A user preference transfer and drift network for cross-city next POI recommendation. International Journal of Intelligent Systems, 2023, 1–17. https://doi.org/10.1155/2023/9091570CrossRef

23.

Chen, Z., Li, J., Cheng, L., & Liu, X. (2023). Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation (Vol. 334).

24.

Chen, C., Wu, H., Su, J., Lyu, L., Zheng, X., & Wang, L. (2022). Differential private knowledge transfer for privacy-preserving cross-domain recommendation. In WWW 2022—Proceedings of ACM web conference 2022 (pp. 1455–1465). https://doi.org/10.1145/3485447.3512192

25.

Batista, E., Martínez-Ballesté, A., & Solanas, A. (2022). Privacy-preserving process mining: A microaggregation-based approach. Journal of Information Security Application, 68, 103235. https://doi.org/10.1016/j.jisa.2022.103235

26.

Li, Y., et al. (2022). Fairness in recommendation: A survey (Vol. 1, no. 1, pp. 1–38) [online]. Available http://arxiv.org/abs/2205.13619

27.

Cao, J., Li, S., Yu, B., Guo, X., Liu, T., & Wang, B. (2023). Towards universal cross-domain recommendation. In WSDM 2023—Proceedings of 16th ACM international conference on web search data mining (pp. 78–86). https://doi.org/10.1145/3539597.3570366

28.

Zegers, J., Zogopoulos, V., & Verhees, D. (2022). Recommender systems for personalized work instructions. Procedia CIRP, 109, 496–501. https://doi.org/10.1016/j.procir.2022.05.284CrossRef

29.

Shandilya, R., Sharma, S., & Wong, J. (2022). MATURE-Food: Food recommender system for mandatory feature choices a system for enabling digital health. International Journal of Information Management Data Insights, 2(2), 100090. https://doi.org/10.1016/j.jjimei.2022.100090CrossRef

30.

Tsai, K.-P., Yang, F.-C., & Tang, C.-Y. (2022). Multiagent mobility and lifestyle recommender system for individuals with visual impairment. Neuroscience Informatics, 2(4), 100077. https://doi.org/10.1016/j.neuri.2022.100077CrossRef

31.

Chheda, R., Bohara, D., Shetty, R., Trivedi, S., & Karani, R. (2023). ScienceDirect music recommendation based on affective image content analysis music recommendation based on affective content. Procedia Computer Science, 218, 383–392. https://doi.org/10.1016/j.procs.2023.01.021CrossRef

32.

Yan, D., Zhao, Y., Yang, Z., Jin, Y., & Zhang, Y. (2022). FedCDR: Privacy-preserving federated cross-domain recommendation. Digital Communications Networks, 8(4), 552–560. https://doi.org/10.1016/j.dcan.2022.04.034CrossRef

33.

Bharati, S., Hossain, M. R., Podder, P., & Surya, V. B. Federated learning: Applications, challenges and future directions.

34.

Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based System, 216, 106775. https://doi.org/10.1016/j.knosys.2021.106775CrossRef

35.

Ogunseyi, T. B., Avoussoukpo, C. B., & Jiang, Y. (2021). Privacy-preserving matrix factorization for cross-domain recommendation. IEEE Access, 9(i), 91027–91037. https://doi.org/10.1109/ACCESS.2021.3091426

36.

https://www.tensorflow.org/federated

37.

https://en.wikipedia.org/wiki/Matrix_factorization_(recommender_systems)

Titel: Federated Learning for Personalized Tourism Promotion: Balancing Recommendation Accuracy and User Privacy
verfasst von: S. Amutha
P. Salini
Verlag: Springer Nature Singapore
Buch: Innovative Computing and Communications
Print ISBN: 978-981-9741-51-9

Electronic ISBN: 978-981-9741-52-6

Copyright-Jahr: 2025
DOI: https://doi.org/10.1007/978-981-97-4152-6_28

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